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Nature methods (allikas)

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Kirjeid leitud 5
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Author Correction : DOME : recommendations for supervised machine learning validation in biologyWalsh, Ian; Fishman, Dmytro; Garcia-Gasulla, Dario; Titma, Tiina; Pollastri, GianlucaNature methods2021 / p. 1409–1410 : ill https://doi.org/10.1038/s41592-021-01304-2 Journal metrics at Scopus Article at Scopus

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Seotud publikatsioonid 1
- DOME : recommendations for supervised machine learning validation in biology
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Author Correction: SciPy 1.0 : fundamental algorithms for scientific computing in Python (Nature Methods, (2020), 17, 3, (261-272), 10.1038/s41592-019-0686-2)Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, JonathanNature Methods2020 / p. 352 https://doi.org/10.1038/s41592-020-0772-5 Journal metrics at Scopus Article at Scopus Journal metrics at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- SciPy 1.0 : fundamental algorithms for scientific computing in Python
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DOME : recommendations for supervised machine learning validation in biologyWalsh, Ian; Fishman, Dmytro; Garcia-Gasulla, Dario; Titma, Tiina; Pollastri, Gianluca; Harrow, Jennifer; Psomopoulos, Fotis E.; Tosatto, Silvio C. E.Nature methods2021 / p. 1122–1127 https://doi.org/10.1038/s41592-021-01205-4 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- Author Correction : DOME : recommendations for supervised machine learning validation in biology
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Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic referencesPool, Allan-Hermann; Poldsam, Helen; Chen, Sisi; Thomson, Matt; Oka, YukiNature Methods2023 / p. 1506 - 1515 https://doi.org/10.1038/s41592-023-02003-w Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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SciPy 1.0 : fundamental algorithms for scientific computing in PythonVirtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, PearuNature methods2020 / p. 261-272 https://doi.org/10.1038/s41592-019-0686-2 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
Seotud publikatsioonid 1
- Author Correction: SciPy 1.0 : fundamental algorithms for scientific computing in Python (Nature Methods, (2020), 17, 3, (261-272), 10.1038/s41592-019-0686-2)

Kirjeid leitud 5, kuvan 1 - 5

1. Changing nature of work
2. nature
3. nature conservation
4. nature inspired
5. nature protection
6. nature tourism
7. nature values
8. nature writing
9. nature-based composite
10. nature-based solutions
11. nature-inspired tribology
12. nature-like fishway
13. values of nature
14. ab-initio methods
15. active learning methods
16. algebraic methods
17. algebraic/geometric methods
18. an ensemble of compression methods
19. analysis methods
20. analytical methods
21. approximate methods
22. approximation methods
23. assessment methods
24. Backstepping methods
25. biology experimental methods
26. calculation methods
27. Cesaro methods
28. chemometric methods
29. computational design methods
30. computational methods
31. control methods
32. decontamination methods
33. Design and assessment methods
34. design methods
35. detection methods
36. diagnostics methods
37. didactic methods
38. digital methods
39. electronic structure methods
40. Euler-Knopp methods
41. excavation methods
42. extraction and detection methods
43. finite-element (FE) methods
44. finite-element methods
45. finite-element methods (FEM)
46. flow assessment methods
47. formal methods
48. frontier methods
49. Gauss-Newton-type methods
50. Gauss–Newton-type methods
51. generalized Nörlund methods
52. geometric methods
53. integration methods
54. interaction methods
55. interactive teaching methods
56. interpretation methods
57. invasive methods
58. inverse methods
59. inverse methods for engineering applications
60. linear and nonlinear unsupervised methods
61. material characterization methods
62. Mathematical methods
63. matrix methods
64. methods
65. methods of measuring
66. methods of study
67. methods: data analysis
68. Methods: statistical
69. mixed methods
70. mixed-methods
71. model-free methods
72. modulation methods
73. Monte Carlo methods
74. nearest neighbor methods
75. numerical methods
76. optimization methods
77. outlier’s detection methods
78. physico-chemical methods
79. phytoplankton detection methods
80. plan-driven methods
81. polynomial methods
82. practical teaching methods
83. prediction methods
84. pseudospectral methods
85. qualitative methods
86. qualitative research methods
87. quantitative methods
88. regular matrix methods
89. research methods
90. sampling methods
91. selection methods
92. separation methods
93. shootthrough control methods
94. shoot-through control methods
95. stability prediction methods
96. standardized methods
97. state space methods
98. Statistical methods
99. statistics methods
100. substation testing methods
101. Taguchi methods
102. teaching methods
103. testing methods
104. timber construction (construction methods)
105. tracer gas methods
106. transmission line matrix methods
107. variational methods
108. verification methods
109. visual methods
110. visualization design and evaluation methods
111. X-ray methods
112. X-ray methods B
113. ﬁnite element methods

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